Open-source cloud-native vector database built for billion-scale similarity search
Milvus documents a high-performance vector database at milvus.io/docs for storing, indexing, and searching embedding vectors with metadata filtering and hybrid search. Deployment options include Milvus Lite (`pip install pymilvus` for notebooks/edge), Milvus Standalone (single Docker image), and Milvus Distributed on Kubernetes per milvus.io/docs/v2.6.x/install-overview. Official SDKs include PyMilvus, Go, Java, Node.js, and C#; Zilliz Cloud offers managed Milvus. Architecture separates access, coordinator, worker, and storage layers with object storage backends (MinIO, S3, Azure Blob) per milvus.io/docs/architecture_overview.
Use cases
- Production RAG catalogs at billion-vector scale on Kubernetes
- Recommendation systems combining vector similarity with structured filters
- Notebook prototyping with Milvus Lite then migrating to Standalone/Distributed
- Agent memory layers paired with zilliztech/mcp-server-milvus
- Multimodal embedding search when combined with external embedders
Key features
- HNSW, DiskANN, and other ANN indexes with scalar/JSON metadata filtering
- Milvus Lite, Standalone, and Distributed deployment modes
- Hybrid dense-sparse search and multi-vector support in recent releases
- PyMilvus MilvusClient API for collections, insert, search, and query
- LF AI & Data Foundation project with Zilliz as core maintainer
Who Is It For?
- ML engineers operating large-scale vector search infrastructure
- Platform teams evaluating open-source alternatives to single-vendor vector clouds
- Developers prototyping locally with Milvus Lite before production rollout
Frequently Asked Questions
- Is Milvus the same as Zilliz Cloud?
- Milvus is the open-source project; Zilliz Cloud is the fully managed service built on Milvus.
- Which Python client should I use?
- Docs recommend PyMilvus with MilvusClient for current releases—see milvus.io/docs and pymilvus docs.
- How do agents connect?
- Zilliz maintains mcp-server-milvus (documented at milvus.io/docs/milvus_and_mcp) for MCP clients.
Related
Related
3 Indexed items
Weaviate
Weaviate documents an open-source vector database at docs.weaviate.io/weaviate for storing objects and vector embeddings with semantic, keyword, and hybrid search, RAG, reranking, and agent workflows. The ecosystem includes self-hosted Docker/Kubernetes installs, Weaviate Cloud (console.weaviate.cloud), Query Agent, and Weaviate Embeddings for managed inference. Client libraries include Python (`weaviate-client` v4, requires Weaviate 1.23.7+), TypeScript, Go, and Java with REST, gRPC, and GraphQL APIs per the official documentation.
Qdrant
Qdrant documents an AI-native vector search engine at qdrant.tech/documentation for storing, indexing, and querying high-dimensional vectors with optional payloads, supporting dense, sparse, and multi-vector configurations. Official guides cover Docker/Kubernetes self-hosting, Qdrant Cloud on AWS/GCP/Azure, Hybrid Cloud, Private Cloud, and Qdrant Edge for embedded retrieval. Client libraries include Python (`qdrant-client`), JavaScript/TypeScript (`@qdrant/js-client-rest`), Rust, Go, Java, and .NET with REST and gRPC APIs per the API reference at api.qdrant.tech.
Chroma
Chroma documents an open-source embedding database at docs.trychroma.com for storing and querying vectors, metadata, and full-text fields in Python and JavaScript clients. Official guides cover ephemeral in-memory collections, persistent local storage, self-hosted server deployments, and Chroma Cloud at trychroma.com with authentication tokens. The docs describe collection CRUD, `add`/`query`/`get`/`update`/`delete` APIs, embedding functions (default and third-party), hybrid search, and multitenancy patterns for RAG and agent memory workloads per the documentation index.